Troubleshooting Python Machine Learning [Video]

More Information
Learn
  • Eliminate common data wrangling problems in Pandas and scikit-learn 
  • Defeat regression and classification difficulties in scikit-learn 
  • Troubleshoot advanced models such as Random Forests and SVMs 
  • Wrangling with unsupervised learning and the curse of dimensionality 
  • Solving prediction visualization issues with matplotlib 
  • Explaining your results with the most important insights
  • Visualizing your decision trees
  • Perform common natural language processing featuring engineering tasks
About

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?

Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge.

Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.

All the code and supporting files are available on GitHub at https://github.com/PacktPublishing/Troubleshooting-Python-Machine-Learning

Style and Approach

The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. It is packed full of useful tips and relevant advice. Throughout the course, we maintain a focus on practicality and getting things done, not fancy mathematical theory.

Features
  • Covering common ML problems documented online (leveraging sources such as Stack Overflow, Medium, and GitHub) and solved entirely in this one course.
  • Each video is constructed in a problem-solution format, making it easy to understand the problem and grasp the solution.
  • Tried and tested solutions to solving common problems, while implementing Machine learning with Python
Course Length 3 hours 17 minutes
ISBN 9781788999229
Date Of Publication 26 Apr 2018

Authors

Rudy Lai

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and Cloud computing.

Over the past few years, they have worked with some of the World's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways.
The company lives by its motto: Data -> Intelligence -> Action.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails for prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generates content.

Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which you can learn about reinforcement learning and supervised learning topics in depth and in a commercial setting.

Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.